arXiv Machine Learning July 1, 2026 By Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus

Semantic Leakage and Privacy Preservation in Relay-Assisted Semantic Communications

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arXiv:2606. 31973v1 Announce Type: cross Abstract: Semantic communication (SemCom) has emerged as a promising paradigm in which the transmission of task-relevant information is prioritized over raw data, enabling efficient and robust communication under resource and channel constraints.

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safety

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Diffusion-aided Task-oriented Semantic Communications with Model Inversion Attack

The paper introduces DiffSem, a diffusion-based approach for task‑oriented semantic communications that splits the diffusion process between transmitter‑side self‑noising and receiver‑side reverse denoising. It addresses privacy concerns by reducing model‑inversion attacks while preserving task accuracy, as demonstrated on MNIST, CIFAR‑10, and CelebA datasets. The method achieves higher task performance without enlarging transmitted feature size or increasing semantic leakage.

By Xuesong Wang, Mo Li, Xingyan Shi, Zhaoqian Liu, Shenghao Yang

diffusion safety

arXiv Computation and Language

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LLMs as Linguistic Chameleons: Decoupling Semantics and Structure for Privacy-Preserving Communication

arXiv:2609.23193v1 Announce Type: cross Abstract: As Large Language Model (LLM) APIs become increasingly integrated into privacy-sensitive workflows, ensuring inference-time privacy without compromis...

By Yuzhu Mao, Liang Zhao

llms safety

arXiv Machine Learning

Aug 6

DeepInvert: Semi-Supervised Embedding Inversion Against Obfuscated Language Models

arXiv:2608. 04477v1 Announce Type: cross Abstract: Cloud-based language model services routinely process prompts containing sensitive information.

By Zhicong Huang, Cheng Hong, Tao Wei

llms rag

arXiv Machine Learning

Jun 30

Wireless Backdoor Attack and Defense for Semantic Communications over Multiple Access Channel

arXiv:2606. 30595v1 Announce Type: cross Abstract: Semantic communication (SemCom) aims to preserve semantic meaning and task-oriented information beyond conventional message recovery over wireless channels.

By Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus

arXiv AI

Jun 15

From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models

arXiv:2606. 14210v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in privacy-sensitive domains, where users must balance the risk of data exposure through external APIs against the high computational cost of local deployment.

By Zixuan Gu, Xiaojun Ye, Yang Liu

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jul 15

Reducing information dependency does not cause training data privacy. Adversarially non-robust features do

arXiv:2607. 12354v1 Announce Type: new Abstract: In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks.

By Rasmus Torp, Shailen K. Smith, Adam Breuer

benchmarks safety

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